rrecsys: An R-package for Prototyping Recommendation Algorithms. - Ludovik Çoba; Markus Zanker (RecSys Posters, 2016)

Replication and Reproduction in Recommender Systems Research - Evidence from a Case-Study with the rrecsys Library. - Ludovik Çoba; Markus Zanker (IEA/AIE (1), 2017)

Reproducing and Prototyping Recommender Systems in R. - Ludovik Çoba; Panagiotis Symeonidis; Markus Zanker (IIR, 2017)

Visual Analysis of Recommendation Performance. - Ludovik Çoba; Panagiotis Symeonidis; Markus Zanker (RecSys, 2017)

Novelty-Aware Matrix Factorization Based on Items' Popularity. - Ludovik Coba; Panagiotis Symeonidis; Markus Zanker (AI*IA, 2018)

Exploring Users' Perception of Rating Summary Statistics. - Ludovik Coba; Markus Zanker; Laurens Rook; Panagiotis Symeonidis (UMAP, 2018)

Exploring Users' Perception of Collaborative Explanation Styles. - Ludovik Coba; Markus Zanker; Laurens Rook; Panagiotis Symeonidis (CBI (1), 2018)

Replicating and Improving Top-N Recommendations in Open Source Packages. - Ludovik Coba; Panagiotis Symeonidis; Markus Zanker (WIMS, 2018)

Decision Making of Maximizers and Satisficers Based on Collaborative Explanations. - Ludovik Coba; Markus Zanker; Laurens Rook; Panagiotis Symeonidis (CoRR, 2018)

Decision Making Based on Bimodal Rating Summary Statistics - An Eye-Tracking Study of Hotels. - Ludovik Coba; Markus Zanker; Laurens Rook (ENTER, 2019)

Decision making strategies differ in the presence of collaborative explanations: two conjoint studies. - Ludovik Coba; Laurens Rook; Markus Zanker; Panagiotis Symeonidis (IUI, 2019)

Personalised novel and explainable matrix factorisation. - Ludovik Coba; Panagiotis Symeonidis; Markus Zanker (Data Knowl. Eng., 2019)

Counteracting the filter bubble in recommender systems: Novelty-aware matrix factorization. - Panagiotis Symeonidis; Ludovik Coba; Markus Zanker (Intelligenza Artificiale, 2019)

Improving Time-Aware Recommendations in Open Source Packages. - Panagiotis Symeonidis; Ludovik Coba; Markus Zanker (Int. J. Artif. Intell. Tools, 2019)

Recommending the Video to Watch Next: An Offline and Online Evaluation at YOUTV.de. - Panagiotis Symeonidis; Andrea Janes; Dmitry Chaltsev; Philip Giuliani; Daniel Morandini; Andreas Unterhuber; Ludovik Coba; Markus Zanker (RecSys, 2020)

Choosing between hotels: impact of bimodal rating summary statistics and maximizing behavioral tendency. - Ludovik Coba; Laurens Rook; Markus Zanker (J. Inf. Technol. Tour., 2020)

WebTour 2021 Workshop on Web Tourism. - Tsvi Kuflik; Catalin-Mihai Barbu; Amra Delic; Dmitri Goldenberg; Julia Neidhardt; Ludovik Coba; Markus Zanker (WSDM, 2021)

A historical perspective of explainable Artificial Intelligence. - Roberto Confalonieri 001; Ludovik Coba; Benedikt Wagner; Tarek R. Besold (WIREs Data Mining Knowl. Discov., 2021)

RecoXplainer: A Library for Development and Offline Evaluation of Explainable Recommender Systems. - Ludovik Coba; Roberto Confalonieri 001; Markus Zanker (IEEE Comput. Intell. Mag., 2022)

Adversarial Sleeping Bandit Problems with Multiple Plays: Algorithm and Ranking Application. - Jianjun Yuan 002; Wei Lee Woon; Ludovik Coba (RecSys, 2023)

Leveraging Causal Inference to Measure the Impact of a Mental Health App on Users' Well-being. - Aleix Ruiz de Villa; Gabriele Sottocornola; Ludovik Coba; Giovanni Maffei; Federico Lucchesi; João Guerreiro; Bartlomiej Skorulski (UMAP, 2023)

Towards a Technical Debt for Recommender System. - Sergio Moreschini; Ludovik Coba; Valentina Lenarduzzi (CoRR, 2023)

Towards a Technical Debt for AI-based Recommender System. - Sergio Moreschini; Valentina Lenarduzzi; Ludovik Coba (TechDebt@ICSE, 2024)

Ranking the causal impact of recommendations under collider bias in k-spots recommender systems. - Aleix Ruiz de Villa; Gabriele Sottocornola; Ludovik Coba; Federico Lucchesi; Bartlomiej Skorulski (Trans. Recomm. Syst., 2024)